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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Atmospheric H 2 observations from the NOAA Cooperative Global Air Sampling Network

Abstract. The NOAA Global Monitoring Laboratory (GML) measures atmospheric hydrogen (H2) in grab samples collected weekly as flask pairs at over 50 sites in the Cooperative Global Air Sampling Network. Measurements representative of background air sampling show higher H2 in recent years at all latitudes. The marine boundary layer (MBL) global mean H2 was 552.8 ppb in 2021, 20.2 ± 0.2 ppb higher compared to 2010. A 10 ppb or more increase over the 2010–2021 average annual cycle was detected in 2016 for MBL zonal means in the tropics and in the Southern Hemisphere. Carbon monoxide measurements in the same-air samples suggest large biomass burning events in different regions likely contributed to the observed interannual variability at different latitudes. The NOAA H2 measurements from 2009 to 2021 are now based on the World Meteorological Organization Global Atmospheric Watch (WMO GAW) H2 mole fraction calibration scale, developed and maintained by the Max Planck Institute for Biogeochemistry (MPI-BGC), Jena, Germany. GML maintains eight H2 primary calibration standards to propagate the WMO scale. These are gravimetric hydrogen-in-air mixtures in electropolished stainless steel cylinders (Essex Industries, St. Louis, MO), which are stable for H2. These mixtures were calibrated at the MPI-BGC, the WMO Central Calibration Laboratory (CCL) for H2, in late 2020 and span the range 250–700 ppb. We have used the CCL assignments to propagate the WMO H2 calibration scale to NOAA air measurements performed using gas chromatography and helium pulse discharge detector instruments since 2009. To propagate the scale, NOAA uses a hierarchy of secondary and tertiary standards, which consist of high-pressure whole-air mixtures in aluminum cylinders, calibrated against the primary and secondary standards, respectively. Hydrogen at the parts per billion level has a tendency to increase in aluminum cylinders over time. We fit the calibration histories of these standards with zero-, first-, or second-order polynomial functions of time and use the time-dependent mole fraction assignments on the WMO scale to reprocess all tank air and flask air H2 measurement records. The robustness of the scale propagation over multiple years is evaluated with the regular analysis of target air cylinders and with long-term same-air measurement comparison efforts with WMO GAW partner laboratories. Long-term calibrated, globally distributed, and freely accessible measurements of H2 and other gases and isotopes continue to be essential to track and interpret regional and global changes in the atmosphere composition. The adoption of the WMO H2 calibration scale and subsequent reprocessing of NOAA atmospheric data constitute a significant improvement in the NOAA H2 measurement records.

Pétron, Gabrielle↗
NASA NTRS2018

Modernizing and Expanding the NASA Space Geodesy Network to Meet Future Geodetic Requirements

NASA maintains and operates a global network of Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), and Global Navigation Satellite System ground stations as part of the NASA Space Geodesy Program. The NASA Space Geodesy Network (NSGN) provides the geodetic products that support Earth observations and the related science requirements as outlined by the US National Research Council (NRC in Precise geodetic infrastructure: national requirements for a shared resource, National Academies Press, Washington, 2010. http://nap.edu/12954, Thriving on our changing planet: a decadal strategy for Earth observation from space, National Academies Press, Washington, 2018. http://nap.edu/24938). The Global Geodetic Observing System (GGOS) and the NRC have set an ambitious goal of improving the Terrestrial Reference Frame to have an accuracy of 1 mm and stability of 0.1 mm per year, an order of magnitude beyond current capabilities. NASA and its partners within GGOS are addressing this challenge by planning and implementing modern geodetic stations colocated at existing and new sites around the world. In 2013, NASA demonstrated the performance of its next-generation systems at the prototype next-generation core site at NASA’s Goddard Geophysical and Astronomical Observatory in Greenbelt, Maryland. Implementation of a new broadband VLBI station in Hawaii was completed in 2016. NASA is currently implementing new VLBI and SLR stations in Texas and is planning the replacement of its other aging domestic and international legacy stations. In this article, we describe critical gaps in the current global network and discuss how the new NSGN will expand the global geodetic coverage and ultimately improve the geodetic products. We also describe the characteristics of a modern NSGN site and the capabilities of the next-generation NASA SLR and VLBI systems. Finally, we outline the plans for efficiently operating the NSGN by centralizing and automating the operations of the new geodetic stations.

Merkowitz, S. M.↗
NASA NTRS2012

Micro/Nanosatellite Mars Network for Global Lower Atmosphere Characterization

To address multiple key challenge areas for robotic exploration of Mars, to achieve scientific goals and reduce risk for future human missions, a micro/nanosatellite constellation for lower atmosphere characterization is proposed. A microsatellite design is discussed that can operate (1) in tandem with another microsat or (2) as a "mother-ship" to deploy a network of nanosatellites (CubeSats). Either configuration of the network would perform radio occultation-based atmospheric measurements. Advantages of the proposed network are low development cost based on an existing microsatellite bus, and proven performance of the bus to date. Continued efforts in miniaturization of instruments are needed to fully enable the mother-ship/nanosat version of the proposed network.

Tinker, Mike L.↗
NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
NASA NTRS1972

Reliability of the global NASCOM network.

Transmission media reliability is an extremely important factor in the design and operation of a real-time communications network. It has had a profound effect not only on the configuration design of the NASCOM network itself, but on the philosophy of command/control of all NASA spacecraft, manned and unmanned. In an effort to obtain the highest possible level of circuit reliability and performance, goals or criteria have been established for each type of circuit. Also, special management techniques have been used in an effort to improve circuit reliability. The results of these efforts, as well as general technological improvements in the transmission media, are illustrated by showing measured composite circuit reliability over the past nine years and actual circuit performance measured during Apollo 8 through 15.

Stelter, L. R.↗
NASA NTRS1972

Free adjustment of a geometric global satellite network (solution MPS-7)

The basic purpose of this experiment was to compute reduced normal equations from the observational data of several different systems described below to combine them eventually with the normal equations of the Wild BC-4 observations taken in the DOD/DOC cooperative worldwide geodetic satellite program and provide station coordinates from a single least squares adjustment. The solution described is a partial one obtained without the use of the BC-4 data. The observational systems combined were the Baker-Nunn simultaneous camera observational systems combined were the Baker-Nunn simultaneous camera observations from the SAO worldwide network, the MOTS and PC-1000 optical observations in North America, miscellaneous camera observations in Europe which were included in the SAO69 solution, and, lastly, a group of optical observations where Baker-Nunn cameras observed simultaneously with MOTS and/or PC-1000 cameras in the previously mentioned group.

Mueller, I. I.↗
NASA NTRS1977

Tracking and data system support for the Viking 1975 mission to Mars. Volume 1: Prelaunch planning, implementation, and testing

The tracking and data acquisition support for the 1975 Viking Missions to Mars is described. The history of the effort from its inception in late 1968 through the launches of Vikings 1 and 2 from Cape Kennedy in August and September 1975 is given. The Viking mission requirements for tracking and data acquisition support in both the near earth and deep space phases involved multiple radar tracking and telemetry stations, and communications networks together with the global network of tracking stations, communications, and control center. The planning, implementation, testing and management of the program are presented.

Mudgway, D. J.↗
NASA NTRS1986

Creation of a global geodetic network using Mark III VLBI

The positions of 15 permanent VLBI stations have been determined using Mark III with one-sigma uncertainties of less than 5 cm except for three stations in the Pacific. 46070 delay/delay rate observations acquired by the Crustal Dynamics Project and Polaris/IRIS from 1980-84 were included in a least squares solution to estimate the station positions, 44 radio source positions, and earth orientation parameters.

Ma, Chopo↗
NASA NTRS1998

JPL Global GPS Network

The research described in this viewgraph was carried out by the Jet Propulsion Laboratory (JPL), California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA). JPL currently operates more than 55 permanent, continuously operating GPS ground stations for NASA many in conjunction with international and regional agencies. The data are automatically uploaded from the remote stations, processed, and distributed, with a high degree of reliability.

Moore, A. W.↗
NASA NTRS2007

An Investigation of Synchrony in Transport Networks

The cumulative degree distributions of transport networks, such as air transportation networks and respiratory neuronal networks, follow power laws. The significance of power laws with respect to other network performance measures, such as throughput and synchronization, remains an open question. Evolving methods for the analysis and design of air transportation networks must address network performance in the face of increasing demands and the need to contain and control local network disturbances, such as congestion. Toward this end, we investigate functional relationships that govern the performance of transport networks; for example, the links between the first nontrivial eigenvalue of a network's Laplacian matrix - a quantitative measure of network synchronizability - and other global network parameters. In particular, among networks with a fixed degree distribution and fixed network assortativity (a measure of a network's preference to attach nodes based on a similarity or difference), those with the small eigenvalue are shown to be poor synchronizers, to have much longer shortest paths and to have greater clustering in comparison to those with large. A simulation of a respiratory network adds data to our investigation. This study is a beginning step in developing metrics and design variables for the analysis and active design of air transport networks.

Kincaid, Rex K.↗
NASA NTRS2019

Lunar Navigation Beacon Network Using Global Navigation Satellite System Receivers

With the increasing traffic in the lunar regime as part of NASA efforts to return humans to the moon. In order to support these missions, new capabilities are needed to support autonomous navigation and inter-asset communication. Additionally, with maturation and flight demonstration of increasingly capable small satellites, there is an opportunity to embed technology into a small spacecraft as part of companion missions. This paper addresses one such architecture, taking advantage of a lunar lander vehicle to host a companion spacecraft to build out lunar navigation and communication capability. The backbone of this spacecraft is the Navigator GPS receiver. This hardware has continually broken records on high altitude GPS coverage and has the potential to support autonomous navigation at lunar distances. This research proposes a large cubesat built around this technology and catching a ride to the moon via a lander mission. The concept of operations includes the spacecraft deploying prior to the lunar sphere of influence and maneuvering to enter into a lunar orbit. With the Navigator receiver, this spacecraft is capable of a large amount of autonomy, with a limited need for ground-based orbit determination. This spacecraft will fly alongside the lander, acting as a navigation reference during cruise, descent, and post-landing for mission validation. To assess this mission scenario, three aspects are covered in detail herein: the feasibility and mission requirements for entering into a lunar orbit given deployment along a lander surface-bound trajectory, the performance capability of the receiver along this transfer trajectory and in lunar orbit, and the ability to support navigation of the lander itself. These three areas are discussed in detail, providing results that support feasibility of the mission and determination of initial requirements.

Anzalone, Evan J.↗